Multi-attention Recurrent Network for Human Communication Comprehension (1802.00923v1)

Abstract: Human face-to-face communication is a complex multimodal signal. We use words (language modality), gestures (vision modality) and changes in tone (acoustic modality) to convey our intentions. Humans easily process and understand face-to-face communication, however, comprehending this form of communication remains a significant challenge for AI. AI must understand each modality and the interactions between them that shape human communication. In this paper, we present a novel neural architecture for understanding human communication called the Multi-attention Recurrent Network (MARN). The main strength of our model comes from discovering interactions between modalities through time using a neural component called the Multi-attention Block (MAB) and storing them in the hybrid memory of a recurrent component called the Long-short Term Hybrid Memory (LSTHM). We perform extensive comparisons on six publicly available datasets for multimodal sentiment analysis, speaker trait recognition and emotion recognition. MARN shows state-of-the-art performance on all the datasets.

• The paper presents MARN, which integrates a Long-Short Term Hybrid Memory with a Multi-attention Block to capture complex modality-specific and cross-view interactions.
• It extends traditional LSTM models by embedding multiple attentions that effectively model asynchronous dynamics in language, vision, and acoustics across six public datasets.
• MARN's state-of-the-art performance in tasks such as sentiment, emotion, and speaker trait recognition highlights its potential to enhance AI-driven human-computer interactions.

Multi-attention Recurrent Network for Human Communication Comprehension: A Comprehensive Overview

The paper "Multi-attention Recurrent Network for Human Communication Comprehension" by Amir Zadeh et al. presents a sophisticated architecture to tackle the complexities inherent in human multimodal communication. The authors propose the Multi-attention Recurrent Network (MARN), which significantly advances the state-of-the-art in processing and understanding multimodal signals such as language, vision, and acoustics—key components of human communication.

Model Architecture

At the core of MARN is the novel integration of two components: the Long-short Term Hybrid Memory (LSTHM) and the Multi-attention Block (MAB). The LSTHM is an extension of the traditional LSTM, designed to handle view-specific and cross-view dynamics through a hybrid memory mechanism tailored for each modality. It enables each modality to store interactions that are pertinent to that modality, while also preserving the essential modality-specific dynamics. On the other hand, the MAB is responsible for identifying and encoding multiple cross-view dynamics in the form of a neural code. This component introduces the concept of multiple attentions to capture diverse and potentially asynchronous interactions across modalities, reminiscent of the brain's strategy for multimodal integration.

Experimental Evaluation

The authors undertake a rigorous empirical evaluation on six publicly available datasets, encompassing tasks such as multimodal sentiment analysis, speaker trait recognition, and emotion recognition. Strong numerical results are achieved, with MARN demonstrating state-of-the-art performance across all tasks. For instance, in multimodal sentiment analysis on the CMU-MOSI dataset, MARN outperformed previous models with an accuracy of 77.1% in binary sentiment prediction. This robust performance is consistently observed across other datasets like ICT-MMMO, YouTube, and MOUD, highlighting the versatility and efficacy of MARN across different linguistic contexts and communication attributes.

Implications and Future Directions

The implications of MARN are multifaceted. Practically, it enhances AI's ability to interpret complex human communication, opening avenues for improved human-computer interaction systems, including sentiment-driven interfaces and emotion-aware applications. Theoretically, the framework introduces a structured approach for dealing with multimodal data, stressing the significance of both temporal modeling and cross-view dynamics in communication comprehension.

Looking forward, the research invites several avenues for exploration. Future developments might involve further enhancing the complexity of captured dynamics in real-world scenarios or incorporating additional modalities and contextual knowledge. Moreover, refining model training techniques or exploring unsupervised learning paradigms for multimodal communication could be promising directions.

In conclusion, MARN sets a new benchmark in the domain of human communication comprehension. By capturing intricate modality interactions through innovative neural architectures, this work represents a significant step towards equipping AI with a more profound understanding of human communication dynamics.